FAIR Data, Data Catalogs and the Foundations of AI
This talk will inform participants of what FAIR data is and why this framework gaining traction across the pharma industry.
We will discuss how FAIR data principles are being applied to help build Data Catalogs, where data is much easier to find, access and integrate across large organizations.
Next, we will show how advanced statistical methods can be applied to this framework in order to provide advanced analytics and ultimately AI-like capabilities. I will provide specific use case examples from current work that OSTHUS is performing in this area.
Latest literature on FAIR data principles:
Eric Little is Chief Data Officer with Osthus, working on data science and scalable integration standards for pharmaceuticals, biotech, Life Sciences, and related industries.
He received his Ph.D. in Philosophy and Cognitive Science in 2002 from the University at Buffalo, State University of New York.
He has worked in both academia as a professor in several fields, as well as held multiple management positions in the software development industry.
Dr. Little is a recognized expert in semantic technology, Big Data, analytics applications, and formal ontology. Dr. Little has designed and helped to implement semantic technologies and analytics for use in various applied domains including biomedicine, medical device manufacturing, medical fraud, waste, and abuse detection, pharmaceuticals, medical management, threat prediction/mitigation, disaster management, national defense/intelligence, steel production, and petrochemicals.
OSTHUS is a multinational data consultancy headquartered in Aachen, Germany, with data innovation frameworks deployed in 16 of the top 20 big pharmaceutical companies in the world. For two decades, we have worked with executive teams in diverse industries to introduce a culture of innovation that drives enterprise value. We offer a strong track record of driving costs down while increasing the time to market for complex data initiatives. OSTHUS Blueprints serve as the choice methodology for companies seeking to leverage a vendor-agnostic approach in the pursuit of global digitization, artificial intelligence, prescriptive and deep learning efforts.